Kling AI • video
Kling flagship lineup for high-quality creator-facing video generation with strong output fidelity and increasingly production-grade resolution.
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Kling 2.1 results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video API Readiness | 60.0 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video Control | 88.0 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video Quality | 94.0 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video Speed | 83.0 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video Value | 84.0 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SWEN Video Composite | 90.1 | 100.0 | SWEN Video Registry v2026-06-22. Editorial multimodal ranking with modality-specific scoring based on product capability, control, speed, value and integration readiness. |
Kling 2.1 is an AI model developed by Kling AI, classified as a video model. It is a multimodal model, capable of processing text, images, and potentially other media types. As a proprietary model, it is available via Kling AI's cloud API.
Kling 2.1 does not have public per-token pricing available at this time. Some models offer access via enterprise plans or research programs. Check Kling AI's official website for up-to-date availability and pricing.
Kling 2.1 was evaluated on 6 different benchmarks, covering categories like API Readiness, Control, Quality, Speed, Value, video. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Kling 2.1 specializes in video, offering advanced capabilities for creating and processing video content.
In the 2026 AI model ecosystem, Kling 2.1 competes directly with similarly capable models. Kling AI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Kling flagship lineup for high-quality creator-facing video generation with strong output fidelity and increasingly production-grade resolution.
Kling 2.1 does not have public per-token pricing available at this time. Check Kling AI's official website for up-to-date information.
In available benchmarks, Kling 2.1 scored: SWEN Video API Readiness: 60/100, SWEN Video Control: 88/100, SWEN Video Quality: 94/100. See the full table above for a detailed comparison.
No, Kling 2.1 is a proprietary model from Kling AI. It is available via cloud API. For open source alternatives, check our open source model ranking.
Kling 2.1 excels at multimodal tasks including text and vision.
Last updated: June 22, 2026 • View methodology →